Two DNN models map target cavity observables and transmon-cavity parameters (g, ν_q, α) to candidate geometries, recovering designs that match targets within ~5% and ~2% upon re-simulation.
Padamsee,Superconducting radiofrequency technology for accelerators: state of the art and emerging trends (Wiley-VCH, USA, 2023)
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Neural-Network Inverse Design of SRF Cavities and Transmons for Bosonic Quantum Computation
Two DNN models map target cavity observables and transmon-cavity parameters (g, ν_q, α) to candidate geometries, recovering designs that match targets within ~5% and ~2% upon re-simulation.